Instructions to use prithivMLmods/Fire-Detection-Engine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Fire-Detection-Engine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Fire-Detection-Engine") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("prithivMLmods/Fire-Detection-Engine") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Fire-Detection-Engine") - Notebooks
- Google Colab
- Kaggle
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The **Fire-Detection-Engine** is a state-of-the-art deep learning model designed to detect fire-related conditions in images. It leverages the **Vision Transformer (ViT)** architecture, specifically the `google/vit-base-patch16-224-in21k` model, fine-tuned on a dataset of fire and non-fire images. The model is trained to classify images into one of the following categories: "Fire Needed Action," "Normal Conditions," or "Smoky Environment," making it a powerful tool for detecting fire hazards.
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Classification report:
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precision recall f1-score support
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The **Fire-Detection-Engine** is a state-of-the-art deep learning model designed to detect fire-related conditions in images. It leverages the **Vision Transformer (ViT)** architecture, specifically the `google/vit-base-patch16-224-in21k` model, fine-tuned on a dataset of fire and non-fire images. The model is trained to classify images into one of the following categories: "Fire Needed Action," "Normal Conditions," or "Smoky Environment," making it a powerful tool for detecting fire hazards.
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Classification report:
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precision recall f1-score support
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